무선 통신 시스템을 위한 Compressive Sensing = Compressive Sensing for Wireless Communication System
In this paper, we study about compressive sensing technique for wireless communication system. Compressive sensing (CS) has emerged as a new framework for signal acquisition and sensor design that enables a potentially large reduction in the sampling and computation costs for sensing signals that have a sparse or compressible representation. Conventional approaches to sampling signals or images follow Shannon’s celebrated theorem; the sampling rate must be at least twice the maximum frequency present in the signal (the so-called Nyquist rate). In fact, this principle underlies nearly all signal acquisition protocols used in consumer audio and visual electronics, medical imaging devices, radio receivers, and so on.
Unfortunately, in many important and emerging applications, the resulting Nyquist rate is so high that we end up with too many samples and must compress in order to store, process, or transmit them. Solving both problems for improving the performance and mitigating the complexity, many works exploited a CS framework for wideband communication networks. According to Donoho and Candes et al., the CS theory shows the innovation that the original signal can be successfully reconstructed from the small number of measurements under the appropriate condition. That is an alternative to Shannon Nyquist sampling for acquisition of sparse or compressible signals that can be well approximated by just elements from an -dimensional basis. Especially, a compressed sampling approach can get the sparse signal at sub-Nyquist rates; signal reconstruction makes use of the basic pursuit (BP) or some modified methods such as orthogonal matching pursuit (OMP), tree-based OMP (TOMP).
In this paper, we discuss several CS application techniques for wireless communication system. In wireless communication, the CS has been studied in fields of sensing system such as wireless sensor network(WSN), UWB, cognitive radio(CR), and channel estimation. This paper contains the CS algorithm for CR and channel sensing parts. And the CS applications of this paper are described by focusing on signal recovery algorithm called to compressive sampling matching pursuit (CoSaMP). A CoSaMP, one of recovery algorithms using iterative greedy method, has been exploited for several sensing fields due to effective computation and performance guarantee of reconstruction.
Firstly, in this paper, we propose the collaborative spectrum sensing based on compressive sensing for wideband cognitive radio (CR). Basically, we consider multiple CR nodes which cooperatively operate in a multi-hop network. Especially, we propose a quality factor (QF) corresponding to compression performance in order to increase the flexibility between sub-Nyquist rate and recovery algorithm of multiple CR nodes. In propose method, we extracts a QF within CoSaMP recovery algorithm before finishing the whole of process. This system can reduce the iteration number inside the CoSaMP by computing the QF on sampling matrix unlike reconstruction quality indicator (RQI). The RQI used in conventional fashion is a factor that it evaluates the performance of signal recovery after finishing the whole of recovery algorithm. However, this system distinguishes the signal quality by using information for QF in identification block and sends a changed command of compression rate or sub-Nyquist rate into the analog-to-information converter (AIC). The whole processing in proposed method raises the accuracy of PSD estimate.
And we secondly propose a parallel-CoSaMP (P-CoSaMP) algorithm. The proposed algorithm is a expanded version of CoSaMP. The P-CoSaMP algorithm can support several proxies with parallel structure in order to expand the number of cases for selecting large coefficients during the signal recovery. This operation helps it to converge into a solution quickly. Also, the proposed algorithm enables us to recover the signal stably at small number of measurements by combining subspace supports of -dimension in parallel. So, we must compare the total computational complexity which involve both multiply and number of iteration of two algorithms because we expect to increase complexity of proposed algorithm with parallel structure.
Thirdly, we propose a P-CoSaMP in part of estimation of several wireless channels in orthogonal frequency division multiplexing (OFDM) systems. Especially, we introduce a channel estimation method based on compressive sensing for OFDM system. And we develop a new channel estimation technique applying P-CoSaMP to that part. The CS-based channel estimation is method to estimate the channel by exploiting a channel’s delay-Doppler sparsity on inserting pilots related to sparse representation and CS recovery at transceiver. So, we confirm that the P-CoSaMP algorithm helps to increase the accuracy of channel estimation or to use smaller number of pilots for performance guarantee in case of several wireless channels. In this paper, we consider three type's channels which are vehicular, dispersive, and super resolute. So, in this paper, we demonstrate the performance of proposed algorithm by comparing with other CS-based algorithm. The P-CoSaMP-based channel estimation shows a performance guarantee for channel estimation even if the small number of pilots is used, and, hence, increase spectral efficiency.
Finally, in this paper, we demonstrate the performance guarantee of proposed algorithm through criterions such as MSE, detection and reconstruction probability, BER, and amount of complexity.
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